Adjusting language models

ABSTRACT

Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for adjusting language models. In one aspect, a method includes accessing audio data. Information that indicates a first context is accessed, the first context being associated with the audio data. At least one term is accessed. Information that indicates a second context is accessed, the second context being associated with the term. A similarity score is determined that indicates a degree of similarity between the second context and the first context. A language model is adjusted based on the accessed term and the determined similarity score to generate an adjusted language model. Speech recognition is performed on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority from U.S. patent application Ser. No. 13/077,106, filed on Mar. 31, 2011, the contents of which are incorporated herein by reference in their entirety. Prior application Ser. No. 13/077,106 claims priority to U.S. Provisional Application No. 61/428,533, filed on Dec. 30, 2010. The entire contents of U.S. Provisional Application No. 61/428,533 are also incorporated herein by reference.

BACKGROUND

The use of speech recognition is becoming more and more common. As technology has advanced, users of computing devices have gained increased access to speech recognition functionality. Many users rely on speech recognition in their professions and in other aspects of daily life.

SUMMARY

In one aspect, a method includes accessing audio data; accessing information that indicates a first context, the first context being associated with the audio data; accessing at least one term; accessing information that indicates a second context, the second context being associated with the term; determining a similarity score that indicates a degree of similarity between the second context and the first context; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, where the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.

Implementations can include one or more of the following features. For example, the adjusted language model indicates a probability of an occurrence of a term in a sequence of terms based on other terms in the sequence. Adjusting a language model includes accessing a stored language model and adjusting the stored language model based on the similarity score. Adjusting the accessed language model includes adjusting the accessed language model to increase a probability in the language model that the accessed term will be selected as a candidate transcription for the audio data. Adjusting the accessed language model to increase a probability in the language model includes changing an initial weighting value assigned to the term based on the similarity score. The method includes determining that the accessed term was entered by a user, the audio data encodes speech of the user, the first context includes the environment in which the speech occurred, and the second context includes the environment in which the accessed term was entered. The information that indicates the first context and the information that indicates the second context each indicate a geographic location. The information that indicates a first context and information that indicates the second context each indicate a document type or application type. The information indicating the first context and the information indicating the second context each include an identifier of a recipient of a message. The method includes identifying at least one second term related to the accessed term, the language model includes the second term, and adjusting the language model includes assigning a weighting value to the second term based on the similarity score. The accessed term was recognized from a speech sequence and the audio data is a continuation of the speech sequence.

Other implementations of these aspects include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 3 are diagrams illustrating examples of systems in which a language model is adjusted.

FIG. 2A is a diagram illustrating an example of a representation of a language model.

FIG. 2B is a diagram illustrating an example of a use of an acoustic model with the language model illustrated in FIG. 2A.

FIG. 4 is a flow diagram illustrating a process for adjusting a language model.

FIG. 5 is a block diagram of computing devices.

DETAILED DESCRIPTION

Speech recognition can be performed by adjusting a language model using context information. A language model indicates probabilities that words will occur in a speech sequence based on other words in the sequence. In various implementations, the language model can be adjusted so that when recognizing speech, words that have occurred in a context similar to the current context have increased probabilities (relative to their probabilities in the unadjusted language model) of being selected as transcriptions for the speech. For example, a speech recognition system can identify a location where a speaker is currently speaking A language model of the speech recognition system can be adjusted so that words that were previously typed or spoken near that location can be increased (relative to their values in the unadjusted model), which may result in those words having higher probabilities than words that were not previously typed or spoken nearby. A given context may be defined by one or more various factors related to the audio data corresponding to the speech or related to previously entered words including, for example an active application, a geographic location, a time of day, a type of document, a message recipient, a topic or subject, and other factors.

The language model can also be customized to a particular user or a particular set of users. A speech recognition system can access text associated with a user (for example, e-mail messages, text messages, and documents written or received by the user) and context information that describes various contexts related to the accessed text. Using the accessed text and the associated context, a language model can be customized to increase the probabilities of words that the user has previously used. For example, when the word “football” occurs in e-mail messages written a user, and that user is currently dictating an e-mail message, the speech recognition system increases the likelihood that the word “football” will be transcribed during the current dictation. In addition, based on the occurrence of the word “football” in a similar context to the current dictation, the speech recognition system can identify a likely topic or category (e.g., “sports”). The speech recognition system can increase the probabilities of words related that topic or category (e.g., “ball,” “team,” and “game”), even if the related words have not occurred in previous e-mail messages from the user.

FIG. 1 is a diagram illustrating an example of a system 100 in which a language model is adjusted. The system 100 includes a mobile client communication device (“client device”) 102, a speech recognition system 104 (e.g., an Automated Speech Recognition (“ASR”) engine), and a content server system 106. The client device 102, the speech recognition system 104, and the content server system communicate with each other over one or more networks 108. FIG. 1 also illustrates a flow of data during states (A) to (F).

During state (A), a user 101 of the client device 102 speaks one or more terms into a microphone of the client device 102. Utterances that correspond to the spoken terms are encoded as audio data 105. The client device 102 also identifies context information 107 related to the audio data 105. The context information 107 indicates a particular context, as described further below. The audio data 105 and the context information 107 are communicated over the networks 108 to the speech recognition system 104.

In the illustrated example, the user 101 speaks a speech sequence 109 including the words “and remember the surgery.” A portion of the speech sequence 109 including the words “and remember the” has already been recognized by the speech recognition system 104. The audio data 105 represents the encoded sounds corresponding to the word “surgery,” which the speech recognition system 104 has not yet recognized.

The context information 107 associated with the audio data 105 indicates a particular context. The context can be the environment, conditions, and circumstances in which the speech encoded in the audio data 105 (e.g., “surgery”) was spoken. For example, the context can include when the speech occurred, where the speech occurred, how the speech was entered or received on the device 102, and combinations thereof. The context can include factors related to the physical environment (such as location, time, temperature, weather, or ambient noise). The context can also include information about the state of the device 102 (e.g., the physical state and operating state) when the speech encoded in the audio data 105 is received.

The context information 107 can include, for example, information about the environment, conditions, and circumstances in which the speech sequence 109 occurred, such as when and where the speech was received by the device 102. For example, the context information 107 can indicate a time and location that the speech sequence 109 was spoken. The location can be indicated in one or more of several forms, such as a country identifier, street address, latitude/longitude coordinates, and GPS coordinates. The context information 107 can also describe an application (e.g., a web browser or e-mail client) that is active on the device 102 when the speech sequence 109 was spoken, a type of document being dictated (e.g., an e-mail message or text message), an identifier for one or more particular recipients (e.g., joe@example.com or Dan Brown) of a message being composed, and/or a domain (e.g., example.com) for one or more message recipients. Other examples of context information 107 include the whether the device 102 is moving or stationary, the speed of movement of the device 102, whether the device 102 is being held or not, a pose or orientation of the device 102, whether or not the device 102 is connected to a docking station, the type of docking station to which the client device 102 is connected, whether the device 102 is in the presence of wireless networks, what types of wireless networks are available (e.g., cellular networks, 802.11, 3G, and Bluetooth), and/or the types of networks to which the device 102 is currently connected. In some implementations, the context information 107 can include terms in a document being dictated, such as the subject of an e-mail message and previous terms in the e-mail message. The context information 107 can also include categories of terms that occur in a document being dictated, such as sports, business, or travel.

During state (B), the speech recognition system 104 accesses (i) at least one term 111 and (ii) context information 112 associated to the term 111. The accessed term 111 can be one that occurs in text related to the user 101, for example, text that was previously written, dictated, received, or viewed by the user 101. The speech recognition system 104 can determine that a particular term 111 is related to the user before accessing the term 111. The context information 112 can indicate, for example, a context in which the term 111 occurs and/or the context in which the accessed term 111 was entered. A term can be entered, for example, when it is input onto the device 102 or input into a particular application or document. A term can be entered by a user in a variety of ways, for example, through speech, typing, or selection on a user interface.

The speech recognition system 104 can access a term 111 and corresponding context information 112 from a content server system 106. The content server system 106 can store or access a variety of information in various data storage devices 110 a-110 f. The data storage devices 110 a-110 f can include, for example, search queries, e-mail messages, text messages (for example, Short Message Service (“SMS”) messages), contacts list text, text from social media sites and applications, documents (including shared documents), and other sources of text. The content server system 106 also stores context information 112 associated with the text, for example, an application associated with the text, a geographic location, a document type, and other context information associated with text. Context information 112 can be stored for documents as a whole, for groups of terms, or individual terms. The context information 112 can indicate any and all of the types of information described above with respect to the context information 107.

As an example, the term 111 includes the word “surgery.” The context information 112 associated with the term 111 can indicate, for example, that the term 111 occurred in an e-mail message sent by the user 101 to a particular recipient “joe@example.com.” The context information 112 can further indicate, for example, an application used to enter the text of the e-mail message including the term 111, whether the term 111 was dictated or typed, the geographic location the e-mail message was written, the date and time the term 111 was entered, the date and time the e-mail message including the term 111 was sent, other recipients of the email message including the term 111, a domain of a recipient (e.g., “example.com”) of the email message containing the term 111, the type of computing device used to enter the term 111, and so on.

The accessed term 111 can be a term previously recognized from a current speech sequence 109, or a term in a document that is currently being dictated. In such an instance, the context for the audio data 105 can be very similar to the context of the accessed term 111.

The speech recognition system 104 can identify the user 101 or the client device 102 to access a term 111 related to the user 101. For example, the user 101 can be logged in to an account that identifies the user 101. An identifier of the user 101 or the client device 102 can be transmitted with the context information 107. The speech recognition system 104 can use the identity to access a term 111 that occurs in text related to the user 101, for example, a term 111 that occurs in an e-mail message composed by the user 101 or a term 111 that occurs in a search query of the user 101.

During state (C), the speech recognition system 104 determines a similarity score that indicates the degree of similarity between the context described in the context information 112 and the context described in the context information 107. In other words, the similarity score indicates a degree of similarity between the context of the accessed term 111 and the context associated with the audio data 105. In the example shown in table 120, several accessed terms 121 a-121 e are illustrated with corresponding similarity scores 122 a-122 e. The similarity scores 122 a-122 e are based on the similarity between the context information for the terms 121 a-121 e (illustrated in columns 123 a-123 c) and the context information 107 for the audio data 105.

Each similarity score 122 a-122 e can be a value in a range, such as a value that indicates the degree that the context corresponding to each term 121 a-121 e (indicated in the context information 123 a-123 c) matches the context corresponding to the audio data 105 (indicated in the context information 107). Terms 121 a-121 e that occur in a context very much like the context described in the context information 107 can have higher similarity scores 122 a-122 than terms 121 a-121 e that occur in less similar contexts. The similarity scores 122 a-122 e can also be binary values indicating, for example, whether the context of the term is in substantially the same context as the audio data. For example, the speech recognition system 104 can determine whether the context associated with a term 121 a-121 e reaches a threshold level of similarity to the context described in the context information 107.

The similarity score can be based on one or more different aspects of context information 112 accessed during state (B). For example, the similarity scores 122 a-122 e can be based on a geographical distance 123 a between the geographic location that each term 121 a-121 e was written or dictated and the geographic location in the context information 107. The context information 123 a-123 c can also include a document type 123 b (e.g., e-mail message, text message, social media, etc.) and a recipient 123 c of a message if the term 121 a-121 e occurred in a message. Additionally, the similarity scores 122 a-122 e can also be based on a date, time, or day of the week that indicates, for example when each term 121 a-121 e was written or dictated, or when a file or message containing the term 121 a-121 e was created, sent, or received. Additional types of context can also described, for example, an application or application type with which the term 121 a-121 e was entered, a speed the mobile device was travelling when the term was entered, whether the mobile device was held when the term was entered, and whether the term was entered at a known location such as at home or at a workplace.

The similarity scores can be calculated in various ways. As an example, a similarity score can be based on the inverse of the distance 123 a between a location associated with a term 121 a-121 e and a location associated with the audio data 105. The closer the location of the term 121 a-121 e is to the location of the audio data 105, the higher the similarity score 122 a-122 e can be. As another example, the similarity score 122 a-122 e can be based on the similarity of the document type indicated in the context information 107. When the user 101 is known to be dictating an e-mail message, the terms 121 a-121 e that occur in e-mail messages can be assigned a high similarity score (e.g., “0.75”), the terms 121 a-121 e that occur in a text message can be assigned a lower similarity score (e.g., “0.5”), and terms that occur in other types of documents can be assigned an even lower similarity score (“0.25”).

Similarity scores 122 a-122 e can be determined based on a single aspect of context (e.g., distance 123 a alone) or based on multiple aspects of context (e.g., a combination of distance 123 a, document type 123 b, recipient 123 c, and other information). For example, when the context information 107 indicates that the audio data 105 is part of an e-mail message, a similarity score 122 a-122 e of “0.5” can be assigned to terms that have occurred in e-mail messages previously received or sent by the user 101. Terms that occur not merely in e-mail messages but particular to e-mail messages sent to the recipient indicated in the context information 107 can be assigned a similarity score 122 a-122 e of “0.6.” Terms that occur in an e-mail message sent to that recipient at the same time of day can be assigned a similarity score 122 a-122 e of “0.8.”

As another example, similarity scores 122 a-122 e can be determined by determining a distance between two vectors. A first vector can be generated based on aspects of the context of the audio data 105, and a second vector can be generated based on aspects of the context corresponding to a particular term 121 a-121 e. Each vector can include binary values that represent, for various aspects of context, whether a particular feature is present in the context. For example, various values of the vector can be assigned either a “1” or “0” value that corresponds to whether the device 102 was docked, whether the associated document is an e-mail message, whether the day indicated in the context is a weekend, and so on. The distance between the vectors, or another value based on the distance, can be assigned as a similarity score. For example, the inverse of the distance or the inverse of the square of the distance can be used, so that higher similarity scores indicate higher similarity between the contexts.

Additionally, or alternately, a vector distance can be determined that indicates the presence or absence of particular words in the context associated with the audio data 105 or a term 121 a-121 e. Values in the vectors can correspond to particular words. When a particular word occurs in a context (for example, occurs in a document that speech encoded in the audio data 105 is being dictated into, or occurs in a document previously dictated by the user 101 in which the term 121 a-121 e also occurs), a value of “1” can be assigned to the portion of the vector that corresponds to that word. A value of “0” can be assigned when the word is absent. The distance between the vectors can be determined and used as described above.

Similarity scores 122 a-122 e can be determined from multiple aspects of context. Measures of similarity for individual aspects of context can be combined additively using different weights, combined multiplicatively, or combined using any other function (e.g., minimum or maximum). Thresholds can be used for particular context factors, such as differences in time between the two contexts and differences in distance. For example, the similarity score 122 a-122 e (or a component of the similarity score 122 a-122 e) can be assigned a value of “1.0” if distance is less than 1 mile, “0.5” if less than 10 miles, and “0” otherwise. Similarly, a value of “1.0” can be assigned if the two contexts indicate the same time of day and same day of week, “0.5” if the contexts indicate the same day of week (or both indicate weekday or weekend), and “0” if neither time nor day of the week are the same.

The speech language recognition system 104 can also infer context information that affects the value of various similarity scores. For example, the current word count of a document can affect the similarity score. If the speech recognition system 104 determines that the user 101 has dictated five hundred words in a document, the speech recognition system 104 can determine that the user 101 is not dictating a text message. As a result, the speech recognition system 104 can decrease the similarity score 122 a-122 e assigned to terms 121 a-121 e that occur in text messages relative to the similarity scores 122 a-122 e assigned to terms 121 a-121 e that occur in e-mail messages and other types of documents. As another example, the speech language recognition system 104 can adjust similarity scores 122 a-122 e.

During state (D), the speech recognition system adjusts a language model using the accessed term 121 a and the corresponding similarity score 122 a. A language model can be altered based on the terms 121 a-121 e and corresponding similarity scores 122 a-122 e.

A table 130 illustrates a simple example of an adjustment to a small portion of language model. A language model is described in greater detail in FIGS. 2A and 2B. The table 130 illustrates a component of a language model corresponding to a decision to select a single term as a transcription. For example, the table 130 illustrates information used to select a single term in the speech sequence 109, a term that can be a transcription for the audio data 105.

The table 130 includes various terms 131 a-131 e that are included in the language model. In particular, the terms 131 a-131 e represent the set of terms that can validly occur at a particular point in a speech sequence. Based on other words in the sequence 109 (for example, previously recognized words), a particular portion of the audio data 105 is predicted to match one of the terms 131 131 a-131 e.

In some implementations, the accessed terms and similarity scores do not add terms to the language model, but rather adjust weighting values for terms that are already included in the language model. This can ensure that words are not added to a language model in a way that contradicts proper language conventions. This can avoid, for example, a noun being inserted into a language model where only a verb is valid. The terms 121 b, 121 c, and 121 e are not included in the table 130 because the standard language model did not include them as valid possibilities. On the other hand, the term 121 a (“surgery”) and the term 121 d (“visit”) are included in the table 130 (as the term 131 b and the term 131 d, respectively) because they were included as valid terms in the standard language model.

Each of the terms 131 a-131 e is assigned an initial weighting value 132 a-132 e. The initial weighting values 132 a-132 e indicate the probabilities that the terms 131 a-131 e will occur in the sequence 109 without taking into account the similarity scores 122 a-122 e. Based on the other words in the speech sequence 109 (for example, the preceding words “and remember the”), the initial weighting values 132 a-132 e indicate the probability that each of the terms 131 a-131 e will occur next, at the particular point in the speech sequence 109 corresponding to the audio data 105.

Using the similarity scores 122 a-122 e, the speech recognition system 104 determines adjusted weighting values 133 a-133 e. For example, the speech recognition system 104 can combine the initial weighting values with the similarity scores to determine the adjusted weighting values 133. The adjusted weighting values 133 a-133 e incorporate (through the similarity scores 122 a-122 e) information about the contexts in which the terms 131 a-131 e occur, and the similarity between the contexts of those terms 131 a-131 e and the context of the audio data 105.

Terms that have occurred in one context often have a high probability of occurring again in a similar context. Accordingly, terms that occur in a similar context as to the context of the audio data 105 have a particularly high likelihood of occurring in the audio data 105. The initial weighting values 132 are altered to reflect the additional information included in the similarity scores 122 a-122 e. As a result, the initial weighting values 132 can be altered so that the adjusted weighting values 133 a-133 e are higher than the initial weighting values 132 for terms with high similarity scores 122 a-122 e.

For clarity, the adjusted weighting values 133 a-133 e are shown as the addition of the initial weighting values 132 and corresponding similarity scores 122 a-122 e. But, in other implementations, the adjusted weighting values 133 a-133 e can be determined using other processes. For example, either the initial weighting values 132 or the similarity scores 122 a-122 e can be weighted more highly in a combination. As another example, the similarity scores 122 a-122 e can be scaled according to the frequency that the corresponding term 131 a-131 e occurs in a particular context, and the scaled similarity score 122 a-122 e can be used to adjust the initial weighting values. The similarity scores can be normalized or otherwise adjusted before determining the adjusting weighting values 133 a-133 e.

The adjusted weighting scores 133 a-133 e can represent probabilities associated with the terms 131 a-131 e in the language model. In other words, the terms 131 a-131 e corresponding to the highest weighting values 133 a-133 e are the most likely to be correct transcriptions. The term 131 b (“surgery”) is assigned the highest adjusted weighting value 133 b, with a value of “0.6,” indicating that the language model indicates that the term “surgery” has a higher probability of occurring than the other terms 131 a, 131 c-131 e.

During state (E), the speech recognition system uses the determined language model to select a recognized word for the audio data 105. The speech recognition system 104 uses information from the adjusted language model and from an acoustic model to determine combined weighting values. The speech recognition system uses the combined weighting values to select a transcription for the audio data 105.

The acoustic model can indicate terms that match the sounds in the audio data 105, for example, terms that sound similar to one or more portions of the audio data 105. The acoustic model can also output a confidence value or weighting value that indicates the degree that the terms match the audio data 105.

A table 140 illustrates an example of the output of the acoustic model and the language model and the combination of the outputs. The table includes terms 141 a-141 e, which include outputs from both the language model and the acoustic model. The table 140 also includes weighting values 142 from the acoustic model and the adjusted weighting values 133 of the language model (from the table 130). The weighting values 142 from the acoustic model and the adjusted weighting values 133 of the language model are combined as combined weighting values 144.

The terms with the highest weighting values from both the acoustic model and the language model are include in the table 140. From the acoustic model, the terms with the highest weighting values are the term 141 a (“surging”), the term 141 b (“surgery”), and the term 141 c (“surcharge”). In the table 140, the terms 141 a-141 c are indicated as output of the acoustic model based on the acoustic model weighting values 142 assigned to those terms 141 a-141 c. As output from the acoustic model, the terms 141 a-141 c represent the best matches to the audio data 105, in other words, the terms that have the most similar sound to the audio data 105. Still, a term that matches the sounds encoded in the audio data 105 may not make logical sense or may not be grammatically correct in the speech sequence.

By combining information from the acoustic model with information from the language model, the speech recognition system 104 can identify a term that both (i) matches the sounds encoded in the audio data 105 and (ii) is appropriate in the overall speech sequence of a dictation. From the language model, the terms assigned the highest adjusted weighting values 133 are the term 141 a (“surgery”), the term 141 d (“bus”), and the term 121 e (“visit”). The terms 141 b, 141 d, 141 e are have the highest probability of occurring in the speech sequence based on, for example, grammar and other language rules and previous occurrences of the terms 141 a, 141 d, 141 e. For example, the term 141 b (“surgery”) and the term 141 d (“visit”) previously occurred in a similar context to the context of the audio data 105, and as indicated by the adjusted weighting values 133, have a high probability of occurring in the speech sequence that includes the audio data 105.

For clarity, a very simple combination of the acoustic model weighting values and the adjusted language model weighting values is illustrated. The acoustic model weighting values 142 and the adjusted language model weighting values 133 a-133 e for each term 141 a-141 e are added together to determine the combined weighting values 143 a-143 e. Many other combinations are possible. For example, the acoustic model weighting values 142 can be normalized and the adjusted language model weighting values 133 a-133 e can be normalized to allow a more precise combination. In addition, the acoustic model weighting values 142 and the adjusted language model weighting values 133 a-133 e can be weighted differently or scaled when determining the combined weighting values 143 a-143 e. For example, the acoustic model weighting values 142 can influence the combined weighting values 143 a-143 e more than the language model weighting values 133 a-133 e, or vice versa.

The combined weighting values 143 a-143 e can indicate, for example, the relative probabilities that each of the corresponding terms 141 a-141 e is the correct transcription for the audio data 105. Because the combined weighting values 143 a-143 e include information from both the acoustic model and the language model, the combined weighting values 143 a-143 e indicate the probabilities that terms 141 a-141 e occur based on the degree that the terms 141 a-141 e match the audio data 105 and also the degree that the terms 141-141 e match expected language usage in the sequence of terms.

The speech recognition system 104 selects the term with the highest combined weighting value 143 as the transcription for the audio data 105. In the example, the term 141 b (“surgery”) has a combined weighting value of “0.9,” which is higher than the other combined weighting values 143 a, 143 c-143 e. Based on the combined weighting values 143 a-143 e, the speech recognition system 104 selects the term “surgery” as the transcription for the audio data 105.

During state (F), the speech recognition system 104 transmits the transcription of the audio data 105 to the client device 102. The selected term 121 a, “surgery,” is transmitted to the client device 102 and added to the recognized words in the speech sequence 109. The user 101 is dictating an e-mail message in a web browser, as the context information 107 indicates, so the client device 102 will add the recognized term “surgery” to the email message being dictated. The speech recognition system 104 (or the content server system 106) can store the recognized term “surgery” (or the entire sequence 109) in association with the context information 107 so that the recently recognized term can be used to adjust a language model for future dictations, including a continuation of the speech sequence 109.

As the user 101 continues speaking, the states (A) through (F) can be repeated with new audio data and new context information. The language model can thus be adapted in real time to be fit the context of audio data accessed. For example, after speaking the words encoded in the audio data 105, the user 101 may change the active application on the client device 102 from a web browser to a word processor and continue speaking. Based on the new context, new similarity scores and adjusted language model weightings can be determined and used to recognize speech that occurs in the new context. In some implementations, the language model can be continuously modified according to the audio data, context of the audio data, the terms accessed, and the contexts associated with the terms.

As described above, the system 100 can use text associated with a particular user 101 (e.g., e-mail messages written by the user 101 or text previously dictated by the user 101) to adapt a language model for the particular user 101. The system can also be used to adapt a language model for all users or for a particular set of users. For example, during state (B) the speech recognition system 104 can access terms and associated context information of a large set of users, rather than limit the terms and contexts accessed to those related to the user 101. The terms and context information can be used to alter the weighting values of a language model for multiple users.

FIG. 2A is a diagram illustrating an example of a representation of a language model 200. In general, a speech recognition system receives audio data that includes speech and outputs one or more transcriptions that best match the audio data. The speech recognition system can simultaneously or sequentially perform multiple functions to recognize one or more terms from the audio data. For example, the speech recognition system can include an acoustic model and a language model 200. The language model 200 and acoustic model can be used together to select one or more transcriptions of the speech in the audio data.

The acoustic model can be used to identify terms that match a portion of audio data. For a particular portion of audio data, the acoustic model can output terms that match various aspects of the audio data and a weighting value or confidence score that indicates the degree that each term matches the audio data.

The language model 200 can include information about the relationships between terms in speech patterns. For example, the language model 200 can include information about sequences of terms that are commonly used and sequences that comply with grammar rules and other language conventions. The language model 200 can be used to indicate the probability of the occurrence of a term in a speech sequence based on one or more other terms in the sequence. For example, the language model 200 can identify which word has the highest probability of occurring at a particular part of a sequence of words based on the preceding words in the sequence.

The language model 200 includes a set of nodes 201 a-201 i and transitions 202 a-202 h between the nodes 201 a-201 i. Each node 201 a-201 i represents a decision point at which a single term (such as a word) is selected in a speech sequence. Each transition 202 a-202 h outward from a node 201 a-201 i is associated with a term that can be selected as a component of the sequence. Each transition 202 a-202 h is also associated with a weighting value that indicates, for example, the probability that the term associated with the transition 202 a-202 h occurs at that point in the sequence. The weighting values can be set based on the multiple previous terms in the sequence. For example, the transitions at each node and the weighting values for the transitions can be determined on the N terms that occur prior to the node in the speech sequence.

As an example, a first node 201 a that represents a decision point at which the first term in a speech sequence is selected. The only transition from node 201 a is transition 202 a, which is associated with the term “the.” Following the transition 202 a signifies selecting the term “the” as the first term in the speech sequence, which leads to the next decision at node 201 b.

At the node 201 b there are two possible transitions: (1) the transition 202 b, which is associated with the term “hat” and has a weighting value of 0.6; and (2) the transition 202 c, which is associated with the term “hats” and has a weighting value of 0.4. The transition 202 b has a higher weighting value than the transition 202 c, indicating that the term “hat” is more likely to occur at this point of the speech sequence than the term “hats.” By selecting the transition 202 a-202 h that has the highest weighting value at each node 201 a-201 i, a path 204 is created that indicates the most likely sequence of terms, in this example, “the hat is black.”

The weighting values of transitions in the language model can be determined based on language patterns in a corpus of example text that demonstrates valid sequences of terms. One or more of the following techniques can be used. Machine learning techniques such as discriminative training can be used to set probabilities of transitions using Hidden Markov Models (“HMMs”). Weighted finite-state transducers can be used to manually specify and build the grammar model. N-gram smoothing can be used to count occurrences of n-grams in a corpus of example phrases and to derive transition probabilities from those counts. Expectation-maximization techniques, such as the Baum-Welch algorithm, can be used to set the probabilities in HMMs using the corpus of example text.

FIG. 2B is a diagram illustrating an example of a use of an acoustic model with the language model illustrated in FIG. 2A. The output of the language model can be combined with output of the acoustic model to select a transcription for audio data. For example, FIG. 2B illustrates the combination of the output from the acoustic model and the language model for the portion of audio data that corresponds to a single term. In particular, FIG. 2B illustrates the output for audio data that corresponds to the term selected by a transition 202 f-202 h from the node 201 d in FIG. 2A. The language model outputs the terms 212 a-212 c and corresponding weighting values 213 a-213 c that are associated with the highest-weighted transitions from the node 201 d. The acoustic model outputs the terms 216 a-216 c that best match the audio data, with corresponding weighting values 217 a-217 c that indicate the degree that the terms 216 a-216 c match the audio data.

The weighting values 213 a-213 c and 217 a-217 c are combined to generate combined weighting values 223 a-223 e, which are used to rank a combined set of terms 222 a-222 e. As illustrated, based on the output of the acoustic model and the language model, the term 222 a “black” has the highest combined weighting value 223 a and is thus the most likely transcription for the corresponding portion of audio data. Although the weighting values 213 a-213 c, 217 a-217 c output by the acoustic model and language model are shown to have equal influence in determining the combined weighting values 223 a-223 e, the weighting values 213 a-213 c, 217 a-217 c can also be combined unequally and can be combined with other types of data.

FIG. 3 is a diagram illustrating an example of a system 300 in which a language model is adjusted. The system 300 includes a client device 302, a speech recognition system 304 n and an index 310 connected through one or more networks 308. FIG. 3 also illustrates a flow of data during states (A) to (G).

During state (A), a user 301 speaks and audio is recorded by the client device 302. For example the user may speak a speech sequence 309 including the words “take the pill.” In the example, the terms “take the” in the speech sequence 309 have already been recognized by the speech recognition system 304. The client device encodes audio for the yet unrecognized term, “pill” as audio data 305. The client device 102 also determines context information 307 that describes the context of audio data 305, for example, the date and time the audio data was recorded, the geographical location in which the audio data was recorded, and the active application and active document type when the audio data was recorded. The client device 302 sends the audio data 305 and the context information 307 to the speech recognition system 304.

During state (B), the speech recognition system 304 accesses one or more terms 311 and associated context information 312. The speech recognition system 304 or another system can create an index 310 of the occurrence of various terms and the contexts in which the terms occur. The speech recognition system 304 can access the index 310 to identify terms 311 and access associated context information 312. As described above, the context information 312 can indicate aspects of context beyond context information included in a document. For example, the context information 312 can indicate the environment, conditions, and circumstances in which terms were entered. Accessed terms 311 can be terms 311 entered by typing, entered by dictation, or entered in other ways.

In some implementations, the speech recognition system 304 can access terms 311 that are known to be associated with the user 301. Alternatively, the speech recognition system 304 can access terms 311 that occur generally, for example, terms 311 that occur in documents associated with multiple users.

During state (C), the speech recognition system determines similarity scores 323 a-323 c that indicate the similarity between the context associated with the audio data 305 and the contexts associated with the accessed terms 311. For example, the table 320 illustrates terms 321 a-321 c accessed by the speech recognition system 304 and associated context information 312. The context information 312 includes a distance from the location indicated in the context information 307, a time the terms 321 a-321 c were entered, and a type of document in which the terms 321 a-321 c occur. Other aspects of the context of the terms can also be used, as described above.

The similarity scores 323 a-323 c can be determined in a similar manner as described above for state (C) of FIG. 1. For example, the similarity scores 323 a-323 c can be assigned to the terms 321 a-321 c so that higher similarity scores 323 a-323 c indicate a higher degree of similarity between the context in which the terms 321 a-321 c occur and the context in which the audio data 305 occurs.

For example, the term 321 a (“surgery”) occurs in an e-mail message, and the audio data 105 also is entered as a dictation for an e-mail message. The term 321 a was entered at a location 0.1 miles from the location that the audio data 105 was entered. Because the document type of the term 321 a and the distance from the location in the context information 307 is small, the term 321 b can be assigned a similarity score 323 a that indicates high similarity. By contrast, the term 321 b occurs in a text message, not in an e-mail message like the audio data 105, and is more distant from the location in the context information 307 than was the term 321 a. The similarity score 323 b thus indicates lower similarity to the context described in the context information 307 than the similarity score 323 a.

During state (D), the speech recognition system 304 identifies terms 326 a-326 e that are related to the terms 321 a-321 c. For example the speech recognition system 304 can identify a set 325 of terms that includes one or more of the terms 321 a-321 c.

The speech recognition system 304 can access sets of terms that are related to a particular category, topic, or subject. For example, one set of terms may relate to “airports,” another set of terms may relate to “sports,” and another set of terms may relate to “politics.” The speech recognition system 304 can determine whether one of the terms 321 a-321 c is included in a set of terms. If a term 321 a-321 c is included in a set, the other words in the set can have a high probability of occurring because at least one term 321 a-321 c related to that topic or category has already occurred.

For example, one set 325 of terms can relate to “medicine.” The set 325 can include the term 321 a (“surgery”) and other related terms 326 a-326 e in the topic or category of “medicine.” The speech recognition system 304 can determine that the set 325 includes one of the terms 321 a-321 c, in this instance, that the set 325 includes the term 321 a (“surgery”). The term 326 b (“pill”) and the term 326 c (“doctor”) are not included in the terms 321 a-321 c, which indicates that the user 301 may not have spoken or written those terms 326 b, 326 c previously. Nevertheless, because the user 301 has previously used in the term 321 a (“surgery”), and because the term 321 a is part of a set 325 of terms related to medicine, other terms 326 a-326 a in the set 325 are also likely to occur. Thus the prior occurrence of the term 321 a (“surgery”) indicates that the term 326 b (“pill”) and the 326 c (“doctor”) have a high probability of occurring in future speech as well.

During state (E), a language model is adjusted using the terms 326 a-326 e in the set 325. In general, a user that has previously written or dictated about a particular topic in one setting is likely to speak about the same topic again in a similar setting. For example, when a user 301 has previous dictated a term related to sports while at home, the user 301 is likely to dictate the other words related to sports in the future when at home.

In general, a language model can be adjusted as described above for state (D) of FIG. 1. By contrast to state (D) of FIG. 1, a language model can be adjusted using not only terms 121 a-121 c that have occurred, but also terms 326 a-326 e that may not have occurred previously but are related to a term 121 a-121 c that has occurred. Thus the probabilities in a language model can be set or altered for the terms 326 a-326 e based on the occurrence of a related term 121 a that occurs in the same set 325 as the terms 326 a-326 e.

A table 330 illustrates terms 331 a-331 e that are indicated by a language model to be possible transcriptions for the audio data 105. Each of the terms 331 a-331 e is assigned a corresponding initial weighting value 332 that indicates a probability that each term 331 a-331 e will occur in the speech sequence 309 based other terms identified in the sequence 309. The table also includes a similarity score 323 b of “0.1” for the term 331 d (“car”) and a similarity score 323 a of “0.3” for the term 331 e (“surgery”).

The term 331 b (“pill”) was not included in the terms 321 a-321 c and thus was not assigned a similarity score during state (C). Because the term 331 b (“pill”) has been identified as being related to the term 331 e (“surgery”) (both terms are included in a common set 325), the term 331 b is assigned a similarity score 333 based on the similarity score 323 b of the term 331 e (“surgery”). For example, term 331 b (“pill”) is assigned the similarity score 333 of “0.3” the same as the term 331 e (“surgery”). Assigning the similarity score 333 to the term 331 b (“pill”) indicates that the term 331 b (“pill”), like the term 331 e (“surgery”), has an above-average likelihood of occurring in the context associated with the audio data 305, even though the term 331 b (“pill”) has not occurred previously in a similar context. A different similarity score 333 can also be determined. For example, a similarity score 333 that is lower than the similarity score 323 a for the term 331 e (“surgery”) can be determined because the term 331 b (“pill”) did not occur previously in text associated with the user 301.

The speech recognition system 304 determines adjusted weighting values 334 a-334 e for the respective terms 331 a-331 e based on the similarity scores 323 a, 323 b, 333 that correspond to the terms 331 e, 331 d, 331 b and the initial weighting values 332. The similarity scores 323 a, 323 b, 333 and initial weighting values 332 can be added, as illustrated, or can be scaled, weighted, or otherwise used to determine the adjusted weighting values 334 a-334 e.

During state (F), the speech recognition system 304 uses the adjusted language model to select a recognized word for the audio data 305. Similar to state (E) of FIG. 1, the speech recognition system 304 can combine the adjusted language model weighting values 334 a-334 e with acoustic model weighting values 342 a-342 c to determine combined weighting values 344 a-344 e, as shown in table 340. For example, for the term 341 b (“pill”), the acoustic model weighting value 342 b of “0.3” and the adjusted language model weighting value 334 b of “0.3” can be combined to determine a combined weighting value 344 b of “0.6”. Because the combined weighting value 344 b is greater than the other combined weighting values 344 a, 344 c-344 e, the speech recognition system 304 selects the term 341 b (“pill”) as the transcription for the audio data 305.

During state (G), the speech recognition system 304 transmits the transcription of the audio data 305 to the client device 302. The client device 302 receives the transcription “pill” as the term in the sequence 309 that corresponds to the audio data 305.

FIG. 4 is a flow diagram illustrating a process 400 for determining a language model. Briefly, the process 400 includes accessing audio data. Information that indicates a first context is accessed. At least one term is accessed. Information that indicates a second context is accessed. A similarity score that indicates a degree of similarity between the second context and the first context is determined. A language model is adjusted based on the accessed term and the determined similarity score to generate an adjusted language model. Speech recognition is performed on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.

In particular, audio data is accessed (402). The audio data can include encoded speech of a user. For example, the audio data can include speech entered using a client device, and the client device can transmit the audio data to a server system.

Information that indicates a first context is accessed (403). The first context is associated with the audio data. For example, the information that indicates the first context can indicate the context in which the speech of a user occurred. The information that indicates the first context can describe the environment in which the audio data was received or recorded.

The first context can be the environment, circumstances, and conditions when the speech encoded in the audio data 105 was dictated. The first context can include a combination of aspects, such as time of day, day of the week, weekend or not weekend, month, year, location, and type of location (e.g., school, theater, store, home, work), how the data was entered (e.g., dictation). The first context can also include the physical state of a computing device receiving the speech, such as whether the computing device is held or not, whether the computing device is docked or undocked, the type of devices connected to the computing device, whether the computing device is stationary or in motion, and the speed of motion of the computing device. The first context can also include the operating state of the computing device, such as an application that is active, a particular application or type of application that is receiving the dictation, the type of document that is being dictated (e.g., e-mail message, text message, text document, etc.), a recipient of a message, and a domain of a recipient of a message.

The information that indicates the first context can indicate or describe one or more aspects of the first context. For example, the information can indicate a location, such as a location where the speech encoded in the audio data was spoken. The information that indicates the first context can indicate a document type or application type, such as a type of a document being dictated or an application that was active when speech encoded in the audio data was received by a computing device. The information that indicates the first context can include an identifier of a recipient of a message. For example, the information indicating the first context can include or indicate the recipient of a message being dictated.

At least one term is accessed (404). The term can be determined to have occurred in text associated with a particular user. The term can be determined to occur in text that was previously typed, dictated, received, or viewed by the user whose speech is encoded in the accessed audio data. The term can be determined to have been entered (including terms that were typed and terms that were dictated) by the user. The term can be a term that was recognized from a speech sequence, where the accessed audio data is a continuation of the speech sequence.

Information that indicates a second context is accessed (405). The second context is associated with the accessed term. The second context can be the environment, circumstances, and conditions present when the term was entered, whether by the user or by another person. The second context can include the same combination of aspects as described above for the first context. More generally, the second context can also be the environment in which the term is known to occur (e.g., in an e-mail message sent at a particular time), even if information about specifically how the term was entered is not available.

The information that indicates the second context can indicate one or more aspects of the second context. For example the information can indicate a location, such as a location associated with the occurrence of the accessed term. The information that indicates the second context can indicate a document type or application type, such as a type of a document in which the accessed term occurs or an application that was active when the accessed term was entered. The information that indicates the second context can include an identifier of a recipient or sender of a message. For example, the information that indicates the second context can include or indicate an identifier a recipient of a message that was previously sent.

A similarity score that indicates a degree of similarity between the second context and the first context is determined (406). The similarity score can be a binary or non-binary value.

A language model is adjusted based on the accessed term and the determined similarity score to generate an adjusted language model (408). The adjusted language model can indicate a probability of an occurrence of a term in a sequence of terms based on other terms in the sequence. The adjusted language model can include the accessed term and a weighting value assigned to the accessed term based on the similarity score.

A stored language model can be accessed, and the stored language model can be adjusted based on the similarity score. The accessed language model can be adjusted by increasing the probability that the accessed term will be selected as a candidate transcription for the audio data. The accessed language model can include an initial weighting value assigned to the accessed term. The initial weighting value can indicate the probability that the term will occur in a sequence of terms. The initial weighting value can be changed based on the similarity score, so that the adjusted language model assigns a weighting value to the term that is different from the initial weighting value. In the adjusted language model, the initial weighting value can be replaced with a new weighting value that is based on the similarity score.

Speech recognition is performed on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data (410).

The process 400 can include determining that the accessed term occurs in text associated with a user. The process 400 can include determining that the accessed term was previously entered by the user (e.g., dictated, typed, etc.). The audio data can be associated with the user (for example, the audio data can encode speech of the user), and a language model can be adapted to the user based on one or more terms associated with the user.

The process 400 can include identifying at least one second term related to the accessed term, the language model includes the second term, and determining the language model includes assigning a weighting value to the second term based on the similarity score. The related term can be a term that does not occur in association with the first context or the second context.

FIG. 5 is a block diagram of computing devices 500, 550 that may be used to implement the systems and methods described in this document, as either a client or as a server or plurality of servers. Computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations described and/or claimed in this document.

Computing device 500 includes a processor 502, memory 504, a storage device 506, a high-speed interface controller 508 connecting to memory 504 and high-speed expansion ports 510, and a low speed interface controller 512 connecting to a low-speed expansion port 514 and storage device 506. Each of the components 502, 504, 506, 508, 510, and 512, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504 or on the storage device 506 to display graphical information for a GUI on an external input/output device, such as display 516 coupled to high-speed interface 508. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 500 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. In one implementation, the memory 504 is a volatile memory unit or units. In another implementation, the memory 504 is a non-volatile memory unit or units. The memory 504 may also be another form of computer-readable medium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for the computing device 500. In one implementation, the storage device 506 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 504, the storage device 506, or memory on processor 502.

Additionally computing device 500 or 550 can include Universal Serial Bus (USB) flash drives. The USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device.

The high-speed interface controller 508 manages bandwidth-intensive operations for the computing device 500, while the low-speed interface controller 512 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 508 is coupled to memory 504, display 516 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 510, which may accept various expansion cards (not shown). In the implementation, low-speed controller 512 is coupled to storage device 506 and low-speed expansion port 514. The low-speed expansion port 514, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device 500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 520, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 524. In addition, it may be implemented in a personal computer such as a laptop computer 522. Alternatively, components from computing device 500 may be combined with other components in a mobile device (not shown), such as device 550. Each of such devices may contain one or more of computing devices 500, 550, and an entire system may be made up of multiple computing devices 500, 550 communicating with each other.

Computing device 550 includes a processor 552, memory 564, an input/output device such as a display 554, a communication interface 566, and a transceiver 568, among other components. The device 550 may also be provided with a storage device, such as a microdrive, solid state storage component, or other device, to provide additional storage. The components 552, 564, 554, 566, and 568 are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

The processor 552 can execute instructions within the computing device 550, including instructions stored in the memory 564. The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors. Additionally, the processor may be implemented using any of a number of architectures. For example, the processor 502 may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor. The processor may provide, for example, for coordination of the other components of the device 550, such as control of user interfaces, applications run by device 550, and wireless communication by device 550.

Processor 552 may communicate with a user through control interface 558 and display interface 556 coupled to a display 554. The display 554 may be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 556 may comprise appropriate circuitry for driving the display 554 to present graphical and other information to a user. The control interface 558 may receive commands from a user and convert them for submission to the processor 552. In addition, an external interface 562 may be provide in communication with processor 552, so as to enable near area communication of device 550 with other devices. External interface 562 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The memory 564 stores information within the computing device 550. The memory 564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 574 may also be provided and connected to device 550 through expansion interface 572, which may include, for example, a SIMM (Single In-line Memory Module) card interface. Such expansion memory 574 may provide extra storage space for device 550, or may also store applications or other information for device 550. Specifically, expansion memory 574 may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, expansion memory 574 may be provide as a security module for device 550, and may be programmed with instructions that permit secure use of device 550. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 564, expansion memory 574, or memory on processor 552 that may be received, for example, over transceiver 568 or external interface 562.

Device 550 may communicate wirelessly through communication interface 566, which may include digital signal processing circuitry where necessary. Communication interface 566 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 568. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 570 may provide additional navigation- and location-related wireless data to device 550, which may be used as appropriate by applications running on device 550.

Device 550 may also communicate audibly using audio codec 560, which may receive spoken information from a user and convert it to usable digital information. Audio codec 560 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 550. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 550.

The computing device 550 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 580. It may also be implemented as part of a smartphone 582, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” “computer-readable medium” refers to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), peer-to-peer networks (having ad-hoc or static members), grid computing infrastructures, and the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, various forms of the flows shown above may be used, with steps re-ordered, added, or removed. Also, although several applications of providing incentives for media sharing and methods have been described, it should be recognized that numerous other applications are contemplated. Accordingly, other implementations are within the scope of the following claims. 

1. A computer-implemented method, comprising: accessing audio data; accessing information that indicates a first context, the first context being associated with the audio data; accessing at least one term; accessing information that indicates a second context, the second context being associated with the term; determining a similarity score that indicates a degree of similarity between the second context and the first context; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score, wherein the weighting value changes a probability for a transition between a first decision state in the language model and a second decision state in the language model; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.
 2. The computer-implemented method of claim 1, wherein the adjusted language model indicates a probability of an occurrence of a term in a sequence of terms based on other terms in the sequence.
 3. The computer-implemented method of claim 1, wherein adjusting a language model comprises accessing a stored language model and adjusting the stored language model based on the similarity score.
 4. The computer-implemented method of claim 3, wherein adjusting the accessed language model comprises adjusting the accessed language model to increase a probability in the language model that the accessed term will be selected as a candidate transcription for the audio data.
 5. The computer-implemented method of claim 4, wherein adjusting the accessed language model to increase a probability in the language model includes changing an initial weighting value assigned to the term based on the similarity score.
 6. The computer-implemented method of claim 1, comprising determining that the accessed term was entered by a user, wherein: the audio data encodes speech of the user; the first context includes the environment in which the speech occurred; and the second context includes the environment in which the accessed term was entered.
 7. The method of claim 1, wherein the information that indicates the first context and the information that indicates the second context each indicate a geographic location.
 8. The method of claim 1, wherein the information that indicates a first context and information that indicates the second context each indicate a document type or application type.
 9. The method of claim 1, wherein the information indicating the first context and the information indicating the second context each include an identifier of a recipient of a message.
 10. The method of claim 1, comprising identifying at least one second term related to the accessed term, wherein the language model includes the second term, and wherein adjusting the language model includes assigning a weighting value to the second term based on the similarity score.
 11. The method of claim 1, wherein the accessed term was recognized from a speech sequence and wherein the audio data is a continuation of the speech sequence.
 12. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context being associated with the audio data; accessing at least one term; accessing information that indicates a second context, the second context being associated with the term; determining a similarity score that indicates a degree of similarity between the second context and the first context; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score, wherein the weighting value changes a probability for a transition between a first decision state in the language model and a second decision state in the language model; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.
 13. The system of claim 12, wherein adjusting a language model comprises accessing a stored language model and adjusting the stored language model based on the similarity score.
 14. The system of claim 13, wherein adjusting the accessed language model comprises adjusting the accessed language model to increase a probability in the language model that the accessed term will be selected as a candidate transcription for the audio data.
 15. The system of claim 14, wherein adjusting the accessed language model to increase a probability in the language model includes changing an initial weighting value assigned to the term based on the similarity score.
 16. The system of claim 12, wherein the operations comprise determining that the accessed term was entered by a user, and wherein: the audio data encodes speech of the user; the first context includes the environment in which the speech occurred; and the second context includes the environment in which the accessed term was entered.
 17. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context being associated with the audio data; accessing at least one term; accessing information that indicates a second context, the second context being associated with the term; determining a similarity score that indicates a degree of similarity between the second context and the first context; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score, wherein the weighting value changes a probability for a transition between a first decision state in the language model and a second decision state in the language model; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.
 18. The non-transitory computer storage medium of claim 17, wherein adjusting a language model comprises accessing a stored language model and adjusting the stored language model based on the similarity score.
 19. The non-transitory computer storage medium of claim 18, wherein adjusting the accessed language model comprises adjusting the accessed language model to increase a probability in the language model that the accessed term will be selected as a candidate transcription for the audio data.
 20. The non-transitory computer storage medium of claim 17, wherein the operations comprise determining that the accessed term was entered by a user, and wherein: the audio data encodes speech of the user; the first context includes the environment in which the speech occurred; and the second context includes the environment in which the accessed term was entered.
 21. A computer-implemented method comprising: transmitting, at a client device, audio data to a server system; identifying a first context of the client device; transmitting information indicating the first context to the server system; and receiving, at the client device, a transcription of at least a portion of the audio data at the client device, the server system having accessed at least one term, accessed information that indicates a second context, the second context being associated with the term, determined a similarity score that indicates a degree of similarity between the second context and the first context, adjusted a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score, wherein the weighting value changes a probability for a transition between a first decision state in the language model and a second decision state in the language model, performed speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data, and transmitted the transcription to the client device.
 22. The computer-implemented method of claim 21, wherein having adjusted a language model comprises having accessed a stored language model and having adjusted the stored language model based on the similarity score.
 23. The computer-implemented method of claim 21, wherein the server system has further determined that the accessed term was entered by a particular user, and wherein: the audio data encodes speech of the user; the first context includes the environment in which the speech occurred; and the second context includes the environment in which the accessed term was entered.
 24. The computer-implemented method of claim 1, wherein the probability for the transition represents a probability for occurrence of the accessed term in a speech sequence.
 25. The computer-implemented method of claim 24, wherein: the first decision state is a first hidden Markov model state in the language model; the second decision state is a second hidden Markov model state in the language model; and adjusting the language model based on the accessed term and the determined similarity score to generate an adjusted language model comprises adjusting the language model such that, in the adjusted language model, the weighting value replaces a previous weighting value indicating a probability for a transition between the first hidden Markov model state and the second hidden Markov model state. 